Privacy Preserving Management Module for Data Cooperative Policy Translation
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Solution Overview
Problem
Current computing architectures face challenges in dynamically configuring processes that involve data from multiple sources with diverse privacy requirements, as existing technologies lack a unified approach to ensure compliance with regulatory requirements and user privacy settings, especially in scenarios where data needs to be shared across different entities.
Innovation Solution
A system and method that utilize a privacy preserving management module (PPMMD) to generate a unified storage-and-execution privacy policy, allowing each entity in a data cooperative to configure custom privacy levels for data storage and execution, and transmit mapping data to configure an execution environment that meets or exceeds the privacy levels of all entities, ensuring data privacy is preserved during processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a unified execution environment is created to process data from multiple sources, then data analytics capability is improved, but privacy compliance complexity increases
Solution Approach 1:
The patent introduces a Privacy Policy Translation Service (PPTS) as an intermediary component that translates between diverse privacy policies from different data sources and a unified execution environment's privacy requirements. This mediator service handles the complexity of privacy compliance by acting as a buffer between heterogeneous privacy requirements and the unified processing environment, thereby enabling data analytics while managing privacy compliance complexity.
Solution Approach 2:
The patent transforms privacy policies from different data sources into standardized parameters that can be processed by the unified execution environment. By converting diverse privacy requirements into a common parameter format through the PPTS, the system enables compatibility across different privacy frameworks while maintaining the ability to perform analytics in a unified environment.
2Reliability
If custom privacy levels are configured for each data source, then data privacy protection is improved, but system configuration complexity increases
Solution Approach 1:
The patent implements a universal privacy policy translation mechanism that can handle multiple types of privacy policies from different data sources through a single PPTS component. This multi-functional approach allows the system to accommodate custom privacy levels for each data source while using a unified translation framework, thereby reducing overall system configuration complexity despite the diversity of privacy requirements.
Solution Approach 2:
The patent segments the privacy management functionality into distinct components: individual data sources maintain their own privacy policies, the PPTS handles translation, and the execution environment enforces unified standards. This segmentation allows each component to operate independently with its own complexity level, enabling custom privacy configuration without overwhelming system-wide complexity.
3Productivity
If data is shared across multiple entities for cooperative analytics, then analytical value is improved, but privacy risk increases
Solution Approach 1:
The PPTS serves as a protective intermediary that mediates data sharing between multiple entities. It translates and enforces privacy policies during the data sharing process, ensuring that analytical value can be achieved through cooperative analytics while privacy risks are managed through systematic policy translation and enforcement at the intermediary layer.
Solution Approach 2:
The patent implements feedback mechanisms where the PPTS continuously monitors and adjusts privacy policy translation based on the requirements of participating entities and the nature of data being shared. This feedback loop ensures that privacy risks are dynamically managed while maintaining the analytical value of cooperative data sharing across multiple entities.
Data Source
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AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support leveraging of data cooperatives to facilitate configuration of execution environments in which processes may be performed using data supplied by various data sources of the data cooperative. Each data source may configure data storage privacy levels and execution privacy levels with their respective users. Information regarding the data storage and execution privacy levels may be utilized to generate mapping data that is used to configure a unified storage-and-execution privacy policy that meets or exceeds the data storage privacy levels configured by each individual data source and their respective users. The unified storage-and-execution policy may then be utilized to configure an execution environment, which may be centralized or distributed across multiple computing devices, and data provided by the data sources may be analyzed in a manner that preserves the privacy of the data.